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Series attributes reflect information that is intrinsic to the series. Accessing a series through its attributes allows us to get the intrinsic properties of the series. Most commonly used attributes are mentioned below:

FunctionDescription
Series.dtype Return the dtype object of the underlying data.
Series.empty Indicates whether DataFrame is empty.
Series.index Returns the index (axis labels) of the Series.
Series.ndim Number of dimensions of the underlying data, by definition 1.
Series.shape Return a tuple of the shape of the underlying data.
Series.size Number of elements in the underlying data.
Series.values Return the actual data in the series as an array.

Lets discuss these attributes in detail:

Series.dtype

The dtype attribute is used to get the dtype object of the given series. Consider the following example.

import pandas as pd

x = pd.Series([10, 20, 30])
y = pd.Series(["abc", "xyz"])

print("dtype of x:", x.dtype)
print("dtype of y:", y.dtype)

The output of the above code will be:

dtype of x: int64
dtype of y: object

Series.empty

The empty attribute is used to check whether the given Series is empty or not.

import pandas as pd
Name = ['John', 'Marry', 'Jo', 'Sam']
x = pd.Series(Name)
y = pd.Series()

print("Is x empty?:", x.empty)
print("Is y empty?:", y.empty)

The output of the above code will be:

Is x empty?: False
Is y empty?: True

Series.index

The index attribute is used to return the index (axis labels) of the Series.

import pandas as pd
Name = ['John', 'Marry', 'Jo', 'Sam']
x = pd.Series(Name)

print("The Series contains:")
print(x)

print("\nThe index (axis labels) are:")
print(x.index)

The output of the above code will be:

The Series contains:
0     John
1    Marry
2       Jo
3      Sam
dtype: object

The index (axis labels) are:
RangeIndex(start=0, stop=4, step=1)

The above result is written in a compact format which can be interpreted as [0, 1, 2, 3].

Series.ndim

The ndim attribute is used to get the dimensions of the given Series, which is by definition should be 1.

import pandas as pd
Colors = ['Red', 'Blue', 'Green', 'White']
x = pd.Series(Colors)

#dimension of x
print("Dimension of x:", x.ndim)

The output of the above code will be:

Dimension of x: 1

Series.shape

The shape attribute can be used to get a tuple of the shape of the underlying data. Consider the following example.

import pandas as pd
Numbers = [60, 55, 62, 58, 78]
y = pd.Series(Numbers)

#shape of y
print("Shape of y:", y.shape)

The output of the above code will be:

Shape of y: (5,)

Series.size

The size attribute is used to get number of elements in the given Series. Consider the below example:

import pandas as pd
Name = ['John', 'Marry', 'Jo', 'Sam']
x = pd.Series(Name)

print("The Series is:")
print(x)

print("\nThe number of elements in the Series:", x.size)

The output of the above code will be:

The Series is:
0     John
1    Marry
2       Jo
3      Sam
dtype: object

The number of elements in the Series: 4

Series.values

The values attribute is used to return the actual data in the series as an array. Consider the following example:

import pandas as pd
Name = ['John', 'Marry', 'Jo', 'Sam']
x = pd.Series(Name)

print("The Series is:")
print(x)

print("\nThe actual data in the series is:")
print(x.values)

The output of the above code will be:

The Series is:
0     John
1    Marry
2       Jo
3      Sam
dtype: object

The actual data in the series is:
['John' 'Marry' 'Jo' 'Sam']